CITATION

Chowdary, J.S., G. Srinivas, T.S. Fousiya, A. Parekh, C. Gnanaseelan, H. Seo, and

J.A. MacKinnon. 2016. Representation of Bay of Bengal upper-ocean salinity in

general circulation models. Oceanography 29(2):38–49, http://dx.doi.org/10.5670/

oceanog.2016.37.

DOI

http://dx.doi.org/10.5670/oceanog.2016.37

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Oceanography | Vol.29, No.238

Representation of Bay of Bengal Upper-Ocean Salinity

in General Circulation Models

BAY OF BENGAL: FROM MONSOONS TO MIXING

Photo credit: N, Suresh KumarOceanography | Vol.29, No.238

By Jasti S. Chowdary, G. Srinivas, T.S. Fousiya, Anant Parekh,

C. Gnanaseelan, Hyodae Seo, and Jennifer A. MacKinnon

Oceanography | June 2016 39

general, sea surface temperature (SST) remains over 28°C in the BoB through-out the year, which is favorable for gen-eration of active convection (Gadgil et  al., 1984; Graham and Barnett, 1987; Sanilkumar et  al., 1994). The shallow halocline and enhanced stratification in the upper ocean are known to maintain the high heat content and SST in the BoB (Shenoi et  al., 2002, Mahadevan et  al., 2016, in this issue). Further, such salin-ity structure influences the evolution of mixed layer temperature in regions of near-surface haline stratification (Rao and Sivakumar, 1999, 2003; Howden and Murtugudde, 2001; Kara et al., 2003; de Boyer Montégut et al., 2004).

Representation of the BoB salinity structure both in coupled and uncoupled (stand-alone) general circulation models (GCMs) has been challenging due to the

complexity arising from the unique phys-ical and dynamical processes in this basin. The East India Coastal Current (EICC) is known to play an important role in main-taining the large-scale mass and salt bal-ances between the BoB and the Arabian Sea (e.g.,  Durand et  al., 2009). Studies suggest that the EICC transports fresh-water southward along the east coast of India at the end of summer, and it reaches the southern tip of India by the end of boreal fall (e.g.,  Durand et  al., 2009; Akhil et al., 2014). Apart from this, the winter monsoon current transports low-salinity BoB water to the Arabian Sea (Vinayachandran et  al., 2005). Previous ocean model studies (e.g.,  Akhil et  al., 2014) have suggested that erosion of the freshwater tongue along the east coast of India is due to vertical processes rather than horizontal advection. Furthermore, observations have revealed the impor-tance of upward pumping of saltier water to the surface to maintain the salt balance in the southern BoB (Vinayachandran et al., 2013). This suggests the importance of both vertical and lateral processes in controlling the salinity budget in the BoB.

Thus, it is essential to properly rep-resent freshwater flux, current systems, and vertical processes (e.g.,  Benshila et al., 2014; Akhil et al., 2014; Wilson and Riser, 2016; Parekh et  al., 2016; Behara and Vinayachandran, 2016, and refer-ences therein) within GCMs so that they simulate realistic surface and subsurface salinity structure in the BoB. Previous modeling studies examined BoB sur-face salinity variability using sensitivity

INTRODUCTIONThe Bay of Bengal (BoB) is a unique tropical basin whose physical properties exhibit extreme variability. This variabil-ity is governed by southwesterly monsoon winds from May to September and north-easterly monsoon winds from October to March (e.g., Schott and McCreary, 2001; Shankar et al., 2002). In the BoB, precip-itation exceeds evaporation (e.g., Prasad, 1997), and adjoining rivers input a large amount of freshwater (e.g., Subramanian, 1993), making the upper ocean in this region substantially fresher compared to other tropical oceans (e.g.,  Sengupta et al., 2006). The low-salinity upper layers maintain stable stratification (Narvekar and Prasanna Kumar, 2006; Agarwal et al., 2012), which is largely controlled by surface freshwater fluxes (e.g.,  Sengupta et  al., 2016; Wilson and Riser, 2016). In

“Representation of the Bay of Bengal salinity structure both in coupled and uncoupled (stand-alone)

general circulation models has been challenging due to the complexity arising from the unique physical and

dynamical processes in this basin.

”.

ABSTRACT. The Bay of Bengal (BoB) upper-ocean salinity is examined in the National Centers for Environmental Prediction-Climate Forecasting System version 2 (CFSv2) coupled model, Modular Ocean Model version 5 (MOM5), and Indian National Centre for Ocean Information Services Global Ocean Data Assimilation System (INC-GODAS). CFSv2 displays a large positive salinity bias with respect to World Ocean Atlas 2013 in the upper 40 m of the water column. The prescribed annual mean river discharge and excess evaporation are the main contributors to the positive bias in surface salinity. Overestimation of salinity advection also contributes to the high surface salinity in the model during summer. The surface salinity bias in MOM5 is smaller than in CFSv2 due to prescribed local freshwater flux and seasonally varying river discharge. However, the bias is higher around 70 m in summer and 40 m in fall. This bias is attributed to excessive vertical mixing in the upper ocean. Despite the fact that representation of salinity in INC-GODAS is more realistic due to data assimilation, the vertical mixing scheme still imposes systematic errors. The small-scale processes that control oceanographic turbulence are not adequately resolved in any of these models. Better parameterizations based on dedicated observational programs may help improve freshwater representation in regional and global models.

Oceanography | Vol.29, No.240

experiments to understand the rela-tive contributions of various processes (Thompson et al., 2006; Vinayachandran and Nanjundiah, 2009; Akhil et al., 2014; Rahaman et al., 2014). Many GCMs have positive sea surface salinity biases of 2–3 psu over the freshwater- dominated northern BoB. However, factors respon-sible for such salinity biases in the BoB (both in the surface and in the sub-surface) are unclear and need exam-ination. Biases in subsurface salinity and temperature in GCMs can alter the ocean circulation, sea level, vertical mix-ing, and the coupling between ocean and atmosphere (e.g., Seo et al., 2009; Brown et  al., 2013). Thus, accurate representa-tion of the vertical structure of salinity in GCMs is important.

In this study, characteristics and mechanisms of BoB upper-ocean (200  m) salinity biases are examined

in multiple models to identify possi-ble areas of model improvement for the BoB. Mechanisms responsible for salin-ity bias are investigated in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) version 2 (CFSv2; Saha et al., 2014; a cou-pled GCM), Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 5 (MOM5; Griffies, 2012; a stand-alone ocean model), and Indian National Centre for Ocean Information Services (INCOIS)-Global Ocean Data Assimilation System (INC-GODAS; Ravichandran et  al., 2013; a data assim-ilation product; see Table  1 for details). This article describes details of different models and data sets used in the study, provides insights into salinity biases in the BoB vertical structure and associ-ated processes, and discusses thoughts for future work.

MODELS AND DATA NCEP-CFSv2 is a fully coupled ocean- atmosphere-land model with advanced physics and increased resolution com-pared to CFSv1 (Saha et al., 2006). Under the National Monsoon Mission of India, CFSv2 has been selected as an opera-tional model for dynamical monsoon prediction over the Indian region (http://www.tropmet.res.in/monsoon/index.php); this model has also been widely used for global- scale forecasts (Saha et al., 2014). The Global Forecast System’s (GFS) atmospheric component has a hor-izontal resolution of T126 (~100 km) and 64 sigma layers vertically (Saha et  al., 2014). The ocean component of CFSv2 is the GFDL-MOM4 model (Griffies et al., 2004). CFSv2 is integrated over a period of 100 years. The last 60 years are utilized for preparing monthly climatology for the present study. More details of the CFSv2

TABLE 1. Forcing, vertical mixing schemes, and various fluxes used in CFSv2, MOM5, and INC-GODAS.

CFSv2 MOM5 INC-GODAS

OGCM GFDL-MOM4 Stand-alone OGCM MOM4

Vertical mixing parameterization

Nonlocal K-profile parameterization (Large et al., 1994)

Nonlocal K-profile parameterization (Large et al., 1994)

Nonlocal K-profile parameterization (Large et al., 1994)

River runoff Annual mean runoff (single time step; Dai and Trenberth, 2002)

Monthly climatology(Dai et al., 2009)

Interannual monthly river discharge (derived from data sets provided by Dai et al., 2009, and Papa et al., 2010)

Data assimilation technique None None 3D VAR

(Derber and Rosati, 1989)

Variables assimilated None None

Temperature and salinity profiles fromArgo, TAO/TRITON, RAMA, PIRATA, XBT, CTD, and NDPB (Ravichandran et al., 2013)

Evaporation (E)Computed from bulk formula using model SST and moisture from atmospheric model (GFS)

Computed from bulk formula using model SST (COREv2)

Computed from bulk formula using model SST

Precipitation (P)Simulated by the GFS through Arakawa–Schubert (SAS) scheme(Pan and Wu, 1995)

Prescribed monthly mean precipitation from COREv2 (Large and Yeager, 2008)

Prescribed from monthly NCEP-R2 precipitation (Kanamitsu et al., 2002)

Wind forcing Simulated by the GFS Six-hourly winds from COREv2 QuikSCAT/DASCAT winds

Model domain and time period

Global and last 60 years of 100 years free run is used

Indo-Pacific region (30°E to 70°W, 60°S to 30°N); one hundred years spinup and interannual simulation from 1948 to 2008

Global and time period of 2003–2012

Horizontal and vertical resolution

Horizontally 0.5° × 0.5° and vertically 10 m in the upper 220 m

Horizontally 0.25° × 0.25° and vertically 10 m in the upper 220 m.

0.5° degree in zonal and meridional; meridional resolution is 0.25° within 10°S to 10°N and vertical resolution is 10 m in the upper 220 m

Radiation fluxes RRTM (Rapid Radiative Transfer Mode) radiation scheme Daily fluxes from COREv2 Daily NCEP-R2 fluxes

Oceanography | June 2016 41

free run are provided in Roxy (2014). The stand-alone ocean model MOM5

is forced with the Coordinated Ocean-ice Reference Experiments version  2 (COREv2) surface forcing data sets for global ocean-ice modeling (Large and Yeager, 2004), developed at the National Center for Atmospheric Research (NCAR). In the model, turbulent heat fluxes are derived from the NCAR bulk formulae using the simulated SST and the prescribed 10 m atmospheric parameters (see Table 1 for details). Monthly clima-tological river discharge at discrete river mouth locations at 1° × 1° global grid (Dai and Trenberth, 2002) is used.

Finally, ocean reanalysis gener-ated by INC-GODAS is also exam-ined (Ravichandran et  al., 2013). INC-GODAS (ocean model is MOM4) is an improved version of NCEP-GODAS (ocean model is MOM3) with a shorter assimilation window (Ravichandran et  al., 2013). Temperature and salin-ity profiles are assimilated at six-hour intervals using all observations from the 10-day assimilation window, and the 3DVAR assimilation technique is used for data assimilation. Temperature and salinity profiles are assimilated from various in situ ocean observa-tional networks, including Argo profil-ing floats, Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON; McPhaden, 1993), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA; McPhaden et  al., 2009), Pilot Research Moored Array in the Atlantic (PIRATA; Servain et  al., 1998) moored buoys, expendable bathythermographs (XBTs), and National Data Buoy Program (NDBP) moor-ings. We note that maintaining such var-ied observational networks (Venkatesan et al., 2013) is crucial if models are to be substantially constrained by data assim-ilation. Ravichandran et  al. (2013) pro-vide more details of INC-GODAS. The model is forced by momentum flux, heat flux, and freshwater flux from the NCEP-DOE atmospheric reanalysis

version  2 (Kanamitsu et  al., 2002; here-after, NCEP  R2). INC-GODAS provides three- dimensional ocean structures and is currently being used to provide ini-tial ocean conditions for the operational model CFSv2 for both seasonal and extended range prediction of the Indian summer monsoon (e.g., Sahai et al., 2013).

Vertical mixing in all three mod-els is prescribed using the standard K-profile parameterization (KPP). The KPP scheme (Large et  al., 1994) is one of the most widely used parameteriza-tion schemes for vertical mixing in ocean GCMs. Unlike bulk mixed layer mod-els, KPP does not assume a well-mixed boundary layer and explicitly predicts an ocean boundary layer depth. Within this boundary layer, the turbulent mix-ing is parameterized using a nonlocal bulk Richardson number and similarity theory. Below the boundary layer, KPP invokes eddy coefficients to parameter-ize vertical mixing due to shear instabil-ity (when model resolved shear produces small Richardson numbers), breaking of a background internal wave field (assumed to be constant and quite weak), and dou-ble diffusion. Table  1 provides details about forcing and specifications of mix-ing schemes for the three models used in the present study.

The simulated fields from the three models, CFSv2, MOM5, and INC-GODAS, are compared with World Ocean Atlas 2013 (WOA13) tempera-ture and salinity products (Locarnini et al., 2013; Zweng et al., 2013). The lat-ter is a reasonable choice, as it has been found to be consistent with RAMA buoy data (not shown) and Argo data (Fousiya et  al., 2015). We also con-sider the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2; Menemenlis et  al., 2008) reanalysis data, the TropFlux air-sea flux prod-uct (Praveen Kumar et  al., 2010), and Global Precipitation Climatology Project (GPCP) monthly precipitation data (Adler et  al., 2003). The ECCO2 prod-uct is used in the present study to esti-mate circulation bias in the models. It

is an automatic differentiation tool that is used to calculate the adjoint code of MITgcm (Marshall et  al., 1997; Azaneu et al., 2014). ECCO2 climatology is con-structed from the period 1992–2007.

In the present study, the three mod-els (CFSv2, MOM5, and INC-GODAS) are validated against the three data sets (WOA13, ECCO2, and TropFlux). To examine the causes for the salinity biases in these models, we compare the Brunt-Vaisala frequency (N2; Gill, 1982), ver-tical shear of horizontal currents, and energy required for mixing (ERM). The latter is a metric of the potential energy of the upper water column (Shenoi et al., 2002). ERM is computed as

ERM = 1–8 (ρb – ρs) gh2,

where g is acceleration due to gravity (9.8 m s–2), ρs is surface layer density (kg m–3), ρb is density at any particular depth beneath the surface mixed layer (kg m–3), and h is mixed layer depth (m). ERM is a proxy for upper-ocean stratification.

UPPER-OCEAN SALINITY BIASES AND POSSIBLE CAUSES Figure  1 shows depth-latitude sections of simulated salinity in CFSv2, MOM5, and INC-GODAS for all seasons, as well as the annual mean and salinity biases (with respect to WOA13) averaged over the BoB (85°E–90°E). CFSv2 shows very high salinity in the upper 20 m north of 14°N for all seasons. The salinity bias is particularly strong in summer (June through September) and fall (October and November), compared to WOA13 climatology (Figure  1c,d). The differ-ence between the orientation of isoha-lines in CFSv2 (model) and WOA13 (data) illustrates discrepancies in salin-ity structure, clearly suggesting that the spatial distribution of freshwater is not well represented in the model. Salinity is more biased to the south of 14°N from the surface to 100 m depth in all seasons except spring (March through May). The model salinity is biased low around 40 m depth during fall and winter (December

Oceanography | Vol.29, No.242

through February). MOM5 shows low salinity in the upper 20 m of the north-ern BoB from March-April-May to October-November (Figure  1f–j). On the other hand, the southern BoB shows a weak bias in MOM5, unlike in CFSv2. However, there is anomalously low salin-ity in the region north of 16°N between 20 m and 60 m depth, with minimum bias in spring and maximum bias in fall (Figure 1g,i). The salinity representation

in INC-GODAS is somewhat similar to MOM5 (Figure  1k–o). The season-ally varying river discharge prescribed in MOM5 and the assimilation of salin-ity profiles in INC-GODAS are likely the main factors for the slightly reduced sur-face salinity biases compared to CFSv2. However, there are still considerable biases in both models during boreal sum-mer and fall. Further in-depth analy-sis is therefore performed to understand

salinity biases in these models during summer and fall over the BoB.

Figure 2 shows vertical profiles of tem-perature, salinity, and density during boreal summer and fall, averaged over the BoB for CFSv2, MOM5, and INC-GODAS and compared with WOA13. In general, the temperature is high near the surface and decreases with depth and vice versa for salinity and density. However, upper-ocean salinity in the BoB is much lower

FIGURE 1. Depth-latitude plots of salinity (mean; shaded, psu) and bias (black contours, psu) averaged between 85°E and 90°E over the Bay of Bengal region in National Centers for Environmental Prediction-Climate Forecast System version 2 (CFSv2; left column), Modular Ocean Model version-5 (MOM5; middle column), and Indian National Centre for Ocean Information Services Global Ocean Data Assimilation System (INC-GODAS; right column) for annual mean (a, f, k), March-April-May (b, g, l), June-July-August-September (c, h, m), October-November (d, i, n), and December-January-February (e, j, o). Biases are calculated with respect to World Ocean Atlas 2013 (WOA13). White contours represent the WOA13 salinity.

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Oceanography | June 2016 43

than in the rest of the tropical Indian Ocean due to the large fresh water influx. CFSv2 displays a large cold bias for the upper 80 m of the water column, a pos-itive bias in salinity from the surface to 100 m depth, and a positive bias in den-sity up to 60 m depth (Figure  2d). In MOM5, the temperature bias is smaller, but salinity and density show a strong negative bias around 70 m depth. In the case of INC-GODAS, biases in tempera-ture and density are high above 60 m (Figure  2f). During fall, the maximum positive bias in salinity is noted around 40 m in MOM5 and CFSv2 and negative bias in INC-GODAS (Figure 2j–l). Biases in temperature, salinity, and density are higher in fall compared to summer.

We next examine freshwater fluxes, water column stability, and current pro-files in the upper ocean in order to inves-tigate the causes of the biases. Figure 3a shows WOA13 climatology and CFSv2 vertical profiles of the Brunt-Vaisala fre-quency (N2) averaged over the BoB for summer. CFSv2 displays a too-weak sta-bility for the upper 100 m as compared

FIGURE 2. Area averaged Bay of Bengal ver-tical profiles of (a) temperature (°C), (b) salin-ity (psu), and (c) density (kg m–3) for CFSv2, MOM5, INC-GODAS, and WOA13 during sum-mer (June-July-August-September). (d–f)  Plots show respective biases. Similar to June through September, October-November vertical profiles of temperature, salinity, and density and their biases are displayed in (g) to (l). Vertical profiles are displayed for the upper 100 m. Bias is calcu-lated with respect to WOA13.

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FIGURE  3. Vertical profiles for summer (June through September) (a) Brunt-Vaisala frequency (N2; in 10–2 s–2), (b) energy required for mixing (ERM; J m–2), and (c) square of the vertical shear of horizontal currents (in 10–4 s–2) averaged over the Bay of Bengal region (80°E–100°E, 6°N–23°N). (d–f)  are the same as (a–c) but for normalized biases. Biases in N2 and ERM (d,e) are calculated with respect to WOA13, whereas bias in vertical shear (f) is calculated with respect to Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2). N2 and ERM (vertical shear) biases are normalized with respect to WOA13 (ECCO2) and provided as percentage values.

Oceanography | Vol.29, No.244

to WOA13, which is clearly seen in the bias (Figure  3d). The ERM profile is consistent with that for the entire trop-ical Indian Ocean (Chowdary et al., 2016b) and confirms the weak stability in CFSv2 (Figure  3b,e). Though upper-ocean salinity is better represented in MOM5 compared to CFSv2, the sta-bility is still too weak in the top 60 m of the water column (Figure  3d,e). In the case of INC-GODAS, the stability is slightly higher than observed and is

better represented compared to CFSv2 and MOM5. This is mainly due to the assimilation of both temperature and salinity profiles. Figure  3c shows verti-cal shear of horizontal currents in CFSv2, MOM5, and INC-GODAS. Shear bias is calculated with respect to the ECCO2 product. It is evident from the bias that vertical shear is higher in all models compared to ECCO2 (Figure 3f). Strong vertical shear and weak stratification favors excessive mixing of low-salinity

surface water with subsurface water. This strong mixing should at least partially explain the salinity bias in both CFSv2 and MOM5. Low-salinity surface water in MOM5 is mixed down below 20 m, resulting in a negative bias around 40 m to 70 m in both the seasons. In the case of CFSv2, biases are consistent with exces-sive mixing, bringing cold, saline water upward and resulting in a positive bias in temperature below 80 m and a reduced bias in salinity below 60 m (Figure 2d,e). This prescribed mixing is likely trig-gered by subcritical Richardson numbers, due to a combination of high shear and low stratification in the model. Though INC-GODAS shows strong vertical shear, higher stability compared to other mod-els limits KPP vertical mixing and hence helps to maintain a more realistic vertical salinity distribution in the BoB.

Figure 4 shows evaporation (E) minus precipitation (P) in CFSv2, MOM5, and INC-GODAS and their biases over the BoB during summer and fall. The east-west gradient in E−P is too strong in CFSv2 compared to observations in both summer and fall. Excess precipitation over evaporation is apparent along the Myanmar coast in CFSv2 during sum-mer, a bias that has also been reported in earlier studies (e.g.,  Parekh et  al., 2016; Chowdary et  al., 2016a). However, pre-cipitation in general is underestimated over most of the BoB, which arises from the biased rainfall simulation from the GFS. This strong positive E−P in the model would contribute to the high salin-ity bias, especially in the surface layer of the BoB in CFSv2 (Figure 2e). Similarly, CFSv2 displays a positive bias in fresh-water flux during fall, which is respon-sible for the positive surface salinity bias (Figure  2k). Consistent with other cou-pled model studies, precipitation in the atmospheric component of CFSv2 con-tains strong biases, with overestimation of evaporation resulting in poor repre-sentation of surface salinity in the BoB (e.g.,  Seo et  al., 2009). MOM5 displays a weak bias in E−P, which is reflected in a weak positive bias in surface salinity

FIGURE 4. Evaporation minus precipitation (shaded; mm day–1) and bias (contours) during summer (June-July-August-September) for (a) TropFlux and Global Precipitation Climatology Project monthly precipitation (GPCP) data products, (b) CFSv2, (c) MOM5, and (d) INC-GODAS. (e–h) The same as (a–d) but for fall (October-November) season.

(a) TROP-GPCP (e) TROP-GPCP

(b) CFSv2 (f) CFSv2

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Oceanography | June 2016 45

(Figures  4c and 2e). INC-GODAS also shows a weak positive bias in E−P in most of the area during both summer and fall (Figure 4c). Evaporation changes in both models are partly dependent on simu-lated SSTs. In response to positive bias in E−P, INC-GODAS shows a weak pos-itive surface salinity bias (Figure 2e). The weak biases in INC-GODAS and MOM5 are partly due to high evaporation, while the strong bias in CFSv2 is due to the bias in the model rainfall. Therefore, accu-rate representation of the net freshwater flux in the coupled models is critical for maintaining a more realistic upper-ocean salinity structure over the BoB, especially at the surface.

Horizontal advection is found to be important for salinity variations in the BoB for most of the year (e.g.,  Rao and Sivakumar, 2003; Sengupta et  al., 2016, and references therein). During summer, ECCO2 shows some regions of north-eastward or southeastward surface cur-rent averaged for the upper 40 m in the BoB (Figure 5a); these are consistent with Ekman transport by southwesterly mon-soonal winds as an important compo-nent of average flow. The southward com-ponent of the surface current is weak in all models west of 86°E (Figure  5b–d). Westward current bias in the central BoB is particularly strong in CFSv2 and MOM5 and slightly weak in INC-GODAS, sug-gesting weaker eastward mean flows in all models. This may also reflect a failure to properly capture the surface-confined Ekman transports. During fall, a basin-scale cyclonic gyre is seen in ECCO2 surface currents (Figure  5e). The south-ward current component near the east coast of India (EICC) is strong in ECCO2 but is weaker in CFSv2, MOM5, and INC-GODAS (Figure 5f–h). Coastal cur-rent systems such as the EICC are not very well captured in models, mainly due to their coarser resolution. Overall, a too-weak cyclonic gyre during fall is evident in many models compared to ECCO2. Figure  6 illustrates mean meridional salinity advection at the southern bound-ary of the BoB (10°N) averaged from

85°E–90°E. As compared to ECCO2, during summer, CFSv2 overestimates salinity advection due to monsoon cur-rents at the surface and in the subsurface (Figure 6a,b). This is consistent with the salinity biases in CFSv2 during summer (Figure  2). MOM5 also displays posi-tive biases in salinity advection but bias is weaker (Figure  6c). In INC-GODAS, the strong negative bias in salinity advec-tion in the upper ocean during spring and positive bias during summer are apparent

(Figure  6d). Discrepancies in salinity advection or inflow and outflow of saline water from the BoB in GCMs also con-tribute to the salinity bias.

DISCUSSIONThe model salinity bias in the BoB could be due to a combination of inaccurate river forcing or freshwater forcing and the incorrect physical representation of the mixing. Our analysis reveals that the salinity bias, especially in the subsurface,

FIGURE 5. JJAS (a) mean ECCO2 surface currents (vectors, m s–1) and mean merid-ional current (shaded, m s–1), (b) bias in CFSv2 surface currents (vectors, m s–1) and mean meridional current (shaded, m s–1), (c) bias in MOM5, and (d) bias in INC-GODAS. (e–h) The same as (a–d) but for October-November (fall season).

(a) ECCO Mean (e) ECCO Mean

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does not arise primarily from the mis-representation of river discharge. For example, we considered models with mean annual (CFSv2) and mean seasonal (MOM5, INC-GODAS) varying river dis-charges, but all three models show similar biases in subsurface structure, suggesting improper vertical mixing in these models is a likely culprit.

Although the stand-alone model (MOM5) was forced with seasonally varying river discharge data, there is no significant reduction in salinity biases, as anticipated by previous modeling stud-ies (e.g., Durand et al., 2011). This issue is important and warrants further inves-tigation. Because river discharges are a result of complex estuarine dynamics that are absent in ocean models, a dedicated observational campaign and sensitiv-ity studies are needed to improve under-standing of freshwater forcing due to river discharge (e.g., Mahadevan et al., 2016, in this issue). For example, river insertion

thickness in the ocean models might be a major factor, as it represents the thick-ness of the water column into which trac-ers from rivers are inserted. Tracer inser-tion is usually done in the mixed layer, so any misrepresentation of mixed layer thickness in the models would result in unrealistic insertion of river water. Another potentially important problem is inability to treat river water properties such as temperature, salinity, and turbid-ity. Of course, it is of utmost importance to develop better river discharge data at higher temporal resolution. When com-bined with in situ observations, satellite altimetry products can be useful for esti-mating river discharges at higher tempo-ral frequency. Overall, a lack of accurate estimates of river runoff (amount, time scale, and properties) has resulted in a lack of consensus in the modeling com-munity on the specification of river run-off; this remains the key challenge for numerical modeling of BoB circulation.

Ocean models with vertical shear parameterizations can successfully repro-duce many general features of the tropical ocean (Packnowski and Philander, 1981). However, the BoB has several unusual features that may influence both the bal-ance of dynamics that set turbulent mix-ing rates and upper-ocean stratification and the most appropriate parameteriza-tion strategies. There are two categories of concern here: ways in which KPP does not accurately represent the in situ verti-cal mixing processes, and processes that are simply not included or represented in existing models. In the former category of concern, one of the primary issues is res-olution. In the BoB, many of the observed fresh water layers are on the order of 10 m thick or even thinner (Lucas et al., 2016, in this issue); models that have a 10 m vertical resolution will never be able to represent such layers accurately, and will always mix “too much.” High priority should be thus given to increasing vertical

FIGURE 6. Monthly mean meridional salinity advection in the upper 100 m (shaded, in 10–6 psu s–1) at the southern boundary of the Bay of Bengal (10°N) averaged from 85°E to 90°E. (a) ECCO2, (b) CFSv2, (c) MOM5, and (d) INC-GODAS. Contours in (b–d) represent bias.

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Oceanography | June 2016 47

resolution in the upper ocean for opera-tional forecast models. Additionally, even with sufficient resolution, it is not clear that KPP, tuned for weaker stratification, will accurately represent entrainment and erosion at the extremely sharp halo-clines found in the upper BoB in certain seasons (Lucas et  al. and Jinadasa et  al., 2016, both in this issue; Sengupta et  al., 2016). Finally, KPP does not include a variety of vertical mixing processes, such as Langmuir turbulence, and this is likely important in mixing both scalers and momentum, though efforts are underway to remedy that omission.

In the second category of concern, which is accurate representation of in situ vertical mixing, a recent observa-tional campaign and numerical pro-cess study have highlighted the impor-tance of a variety of fundamentally three- dimensional phenomena that may play important roles in setting stratifica-tion but are not generally represented in the current generation of GCMs. Much of the surface layer in the BoB is fresh-water stratified, so subject to the “interior ocean mixing” portion of KPP. Internal wave breaking in this layer is likely much more related to locally wind- generated near-inertial internal waves than the background levels assumed in KPP (Johnston et al., 2016, in this issue), and needs to be parameterized differently. A recent approach developed by Jochum et  al. (2013) to explicitly include the effects of near-inertial motions for upper-ocean mixing in the tropics has shown great promise. In general, the shear that produces subcritical Richardson num-bers in the ocean is almost never directly related to the types of mesoscale features resolved by the models presented here; it is instead related to internal waves, inter-nal tides, and frontal processes, each of which must be properly parameterized (MacKinnon et al., 2013).

Many of the observations strongly suggest submesoscale instabilities (see Lucas et al. and Sarkar et al., 2016, both in this issue; Wijesekera et al., in press).

At times, such instabilities induce sig-nificant subduction of one water mass beneath another, increasing local strat-ification and inhibiting vertical mixing; lack of inclusion of such re-stratifying processes may be one reason for the low stratification/ high mixing model biases reported here. Many submesoscale insta-bilities preferentially occur at sharp lat-eral density fronts, of the sort not explic-itly resolved by the models considered here; hence, their effects must be param-eterized in a different manner. Fox-Kemper et  al. (2008a,b, 2011) param-eterized one element in this family of restratifying processes; however, it is not yet clear whether the particular baroclinic instability represented in that scheme is prevalent in the BoB. Ongoing close col-laboration between observationalists, process modelers, and the GCM commu-nity, of the sort facilitated by the Air-Sea Interactions Regional Initiative–Ocean Mixing and Monsoon (ASIRI-OMM) experiments, is imperative for develop-ment of the next generation of success-ful forecast models for the Bay of Bengal.

SUMMARY Upper-ocean stratification in the Bay of Bengal is highly dependent upon salinity, which is substantially lower to the north of 14°N than to the south. Representation of BoB salinity in GCMs is challenging because of the complex ocean processes that need to be accurately captured by the models. In this study, we examined the seasonal mean biases in BoB upper-ocean structure, with a focus on salin-ity fields, in a coupled model (CFSv2), an ocean model (MOM5), and a reanalysis product (INC-GODAS) during summer and fall. A positive salinity bias of about 1 psu is noted north of 14°N in the BoB in CFSv2 over the upper 20 m of the water column. Area-averaged salinity over the BoB is high up to 100 m depth, and there is a maximum bias at the surface. At the same time, the temperature bias is nega-tive. In MOM5, a negative (weakly pos-itive) bias in salinity and density around

70 m (at the surface) is noted, and tem-perature bias is low. The INC-GODAS product displays relatively low bias in temperature and salinity compared to CFSv2 and MOM5.

The processes responsible for upper-ocean salinity biases such as fresh water flux, vertical mixing, and horizontal advection were examined in all mod-els. Excessive evaporation over precipi-tation and prescribed annual mean river discharge in CFSv2 are mainly responsi-ble for the positive salinity bias. MOM5 showed low-salinity bias in the upper 30 m, and this could be due to seasonally varying river discharge and prescribed E−P forcing. However, the salinity bias is higher in the subsurface at around 80 m in summer and 40 m in fall. Salinity is better represented in INC-GODAS in the upper 100 m over the BoB, due to assim-ilation of temperature and salinity pro-files from Argo and other observations. CFSv2 and MOM5 show weaker stabil-ity with positive bias in density within the top few meters. Furthermore, in both models, vertical shear of the horizontal current is higher compared to ECCO2. This suggests excess mixing of low- salinity water from the surface with sub-surface water in MOM5. In the model, a low Richardson number triggers excess vertical mixing. INC-GODAS salinity is better represented over BoB, but ver-tical shear of horizontal currents is not well captured. Furthermore, advection of high-salinity water in CFSv2 at the south-ern boundary of the BoB is consistent with the high-salinity bias. In MOM5, the salinity advection bias is weaker, and the converse is true for INC-GODAS. These results suggest that treatment of the river forcing (vertical mixing, freshwater flux, and lateral advection) is an important factor for improving representation of the BoB salinity fields in coupled and stand-alone ocean models. To estimate biases accurately, high-frequency spatiotempo-ral observations over the BoB from field campaigns such as those described in this volume would be very useful.

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REFERENCESAdler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie,

J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, and others. 2003. The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–pres-ent). Journal of Hydrometeorology 4:1,147–1,167, http://dx.doi.org/10.1175/1525-7541(2003)004 <1147:TVGPCP>2.0.CO;2.

Agarwal, N., R. Sharma, A. Parekh, S. Basu, A. Sarkar, and V.K. Agarwal. 2012. Argo observations of bar-rier layer in the tropical Indian Ocean. Advances in Space Research 50:642–654, http://dx.doi.org/ 10.1016/j.asr.2012.05.021.

Akhil, V.P., F. Durand, M. Lengaigne, J. Vialard, M.G. Keerthi, V.V. Gopalakrishna, C. Deltel, F. Papa, and C. de Boyer Montégut. 2014. A mod-eling study of the processes of surface salin-ity seasonal cycle in the Bay of Bengal. Journal of Geophysical Research 119:3,926–3,947, http://dx.doi.org/10.1002/2013JC009632.

Azaneu, M., R. Kerr, and M. Mata. 2014. Assessment of the representation of Antarctic Bottom Water properties in the ECCO2 reanalysis. Ocean Science 10:923–946, http://dx.doi.org/10.5194/os-10-923-2014.

Behara, A., and P.N. Vinayachandran. 2016. An OGCM study of the impact of rain and river water forc-ing on the Bay of Bengal. Journal of Geophysical Research 121:2,425–2,446, http://dx.doi.org/ 10.1002/2015JC011325.

Benshila, R., F. Durand, S. Masson, R. Bourdallé-Badie, C. de Boyer Montégut, F. Papa, and G. Madec. 2014. The upper Bay of Bengal salin-ity structure in a high resolution model. Ocean Modelling 74:36–52, http://dx.doi.org/10.1016/ j.ocemod.2013.12.001.

Brown, J.N., A. Sen Gupta, J.R. Brown, L.C. Muir, J.S. Risbey, P. Whetton, X. Zhang, B. Ganachaud, B. Murphy, and S.E. Wijffels. 2013. Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific. Climate Change 119:147–161, http://dx.doi.org/10.1007/s10584-012-0603-5.

Chowdary, J.S., A. Parekh, S. Ojha, and C. Gnanaseelan, and R. Kakatkar. 2016a. Impact of upper ocean processes and air-sea fluxes on seasonal SST biases over the tropical Indian Ocean in the NCEP Climate Forecasting System. International Journal of Climatology 36:188–207, http://dx.doi.org/10.1002/joc.4336.

Chowdary, J.S., A. Parekh, G. Srinivas, C. Gnanaseelan, T.S. Fousiya, R. Khandekar, and M.K. Roxy. 2016b. Processes associated with the tropical Indian Ocean subsurface tem-perature bias in a coupled mode. Journal of Physical Oceanography, http://dx.doi.org/10.1175/JPO-D-15-0245.1.

Dai, A., and K.E. Trenberth. 2002. Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. Journal of Hydrometeorology 3:660–687, http://dx.doi.org/ 10.1175/1525-7541(2002)003<0660:EOFDFC> 2.0.CO;2.

Dai, A., T. Qian, K.E. Trenberth, and J.D. Milliman. 2009. Changes in continental freshwater discharge from 1948 to 2004. Journal of Climate 22:2,773–2,791, http://dx.doi.org/ 10.1175/2008JCLI2592.1.

de Boyer Montégut, C., G. Madec, A.S. Fischer, A. Lazar, and D. Iudicone. 2004. Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. Journal of Geophysical Research 109, C12003, http://dx.doi.org/10.1029/2004JC002378.

Derber, J., and A. Rosati. 1989. A global oce-anic data assimilation system. Journal of Physical Oceanography 19:1,333–1,347, http://dx.doi.org/10.1175/1520-0485(1989)019 <1333:AGODAS>2.0.CO;2.

Durand, F., F. Papa, A. Rahman, and S.K. Bala. 2011. Impact of Ganga-Brahmaputra interan-nual discharge variations on Bay of Bengal salin-ity and temperature during 1992–1999 period. Journal of Earth System Science 120:859–872, http://dx.doi.org/10.1007/s12040-011-0118-x.

Durand, F., D. Shankar, F. Birol, and S.S.C. Shenoi. 2009. Spatiotemporal structure of the East India Coastal Current from satellite altimetry. Journal of Geophysical Research 114, C02013, http://dx.doi.org/10.1029/2008JC004807.

Fousiya, T.S., A. Parekh, and C. Gnanaseelan. 2015. Interannual variability of upper ocean stratifica-tion in Bay of Bengal: Observational and modeling aspects. Theoretical and Applied Climatology 1–17, http://dx.doi.org/10.1007/s00704-015-1574-z.

Fox-Kemper, B., G. Danabasoglu, R. Ferrari, S.M. Griffies, R.W. Hallberg, M.M. Holland, M.E. Maltrud, S. Peacock, and B.L. Samuels. 2011. Parameterization of mixed layer eddies: Part III. Implementation and impact in global ocean cli-mate simulations. Ocean Modelling 39:61–78, http://dx.doi.org/10.1016/j.ocemod.2010.09.002.

Fox-Kemper, B., G. Danabasoglu, R. Ferrari, and R.W. Hallberg. 2008a. Parameterizing submeso-scale physics in global climate models. CLIVAR Exchanges 13:3–5.

Fox-Kemper, B., R. Ferrari, and R.W. Hallberg. 2008b. Parameterization of mixed layer eddies: Part I. Theory and diagnosis. Journal of Physical Oceanography 38(6):1,145–1,165, http://dx.doi.org/10.1175/2007JPO3792.1.

Gadgil, S., P.V. Joseph, and N.V. Joshi. 1984. Ocean atmosphere coupling over monsoon regions. Nature 312:141–143, http://dx.doi.org/ 10.1038/312141a0.

Gill, A.E. 1982. Atmosphere-Ocean Dynamics. Academic Press, San Diego, 662 pp.

Graham, N.E., and T.P. Barnett. 1987. Sea surface tem-perature, surface wind divergence, and convec-tion over tropical oceans. Science 238:657–659, http://dx.doi.org/10.1126/science.238.4827.657.

Griffies, S.M. 2012. Elements of the Modular Ocean Model (MOM): 2012 Release. GFDL Ocean Group Technical Report No. 7, Princeton, NJ, 631 pp.

Griffies, S.M., M.J. Harrison, R.C. Pacanowski, and A. Rosati. 2004. A Technical Guide to MOM4. GFDL Ocean Group Technical Report No. 5. Princeton, NJ, NOAA/Geophysical Fluid Dynamics Laboratory, 342 pp.

Howden, D.S., and R. Murtugudde. 2001. Effects of river inputs into the Bay of Bengal. Journal of Geophysical Research 106(C9):19,825–19,844, http://dx.doi.org/10.1029/2000JC000656.

Jinadasa, S.U.P., I. Lozovatsky, J. Planella-Morató, J.D. Nash, J.A. MacKinnon, A.J. Lucas, H.W. Wijesekera, and H.J.S. Fernando. 2016. Ocean turbulence and mixing around Sri Lanka and in adjacent waters of the northern Bay of Bengal. Oceanography 29(2):170–179, http://dx.doi.org/10.5670/oceanog.2016.49.

Jochum, M., G. Briegleb, G. Danabasoglu, W.G. Large, N.J. Norton, S.R. Jayne, M.H. Alford, and F.O. Bran. 2013. The impact of oceanic near-inertial waves on climate. Journal of Climate 26:2,833–2,844, http://dx.doi.org/10.1175/JCLI-D-12-00181.1.

Johnston, T.M.S., D. Chaudhuri, M. Mathur, D.L. Rudnick, D. Sengupta, H.L. Simmons, A. Tandon, and R. Venkatesan. 2016. Decay mech-anisms of near-inertial mixed layer oscillations in the Bay of Bengal. Oceanography 29(2):180–191, http://dx.doi.org/10.5670/oceanog.2016.50.

Kanamitsu, M.W., J.W. Ebisuzaki, S.K. Yang, J.J. Hnilo, M. Fiorino, and G.L. Potter. 2002. NCEP-DEO AMIP-II reanalysis (R-2). Bulletin of American Meteorological Society 83:1,631–1,643, http://dx.doi.org/10.1175/BAMS-83-11-1631.

Kara, A.B., P.A. Rochford, and H.E. Hurlburt. 2003. Mixed layer depth variability over the global ocean. Journal of Geophysical Research 108(C3), 3079, http://dx.doi.org/10.1029/2000JC000736.

Large, W.G., J.C. McWilliams, and S.C. Doney. 1994. Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parame-terization. Reviews of Geophysics 32:363–403, http://dx.doi.org/10.1029/94RG01872.

Large, W., and S. Yeager. 2004. Diurnal to Decadal Global Forcing for Ocean and Sea-Ice Models: The Data Sets and Flux Climatologies. NCAR Technical Note: NCAR/TN-460+STR. CGD Division of the National Center for Atmospheric Research, 112 pp.

Locarnini, R.A., A.V. Mishonov, J.I. Antonov, T.P. Boyer, H.E. Garcia, O.K. Baranova, M.M. Zweng, C.R. Paver, J.R. Reagan, D.R. Johnson, and others. 2013. World Ocean Atlas 2013, Volume 1: Temperature. S. Levitus, ed., A. Mishonov, technical ed., NOAA Atlas NESDIS 73, 40 pp.

Lucas, A.J., J.D. Nash, R. Pinkel, J.A. MacKinnon, A. Tandon, A. Mahadevan, M.M. Omand, M. Freilich, D. Sengupta, M. Ravichandran, and A. Le Boyer. 2016. Adrift upon a salinity- stratified sea: A view of upper-ocean pro-cesses in the Bay of Bengal during the south-west monsoon. Oceanography 29(2):134–145, http://dx.doi.org/10.5670/oceanog.2016.46.

MacKinnon, J.A., L. St. Laurent, and A. Naveira Garabato. 2013. Diapycnal mixing processes in the ocean interior. Pp. 159–184 in Ocean Circulation and Climate: A 21st Century Perspective. G. Siedler, S. Griffies, J. Gould, and J. Church, eds, Academic Press.

Mahadevan, A., G. Spiro Jaeger, M. Freilich, M. Omand, E.L. Shroyer, and D. Sengupta. 2016. Freshwater in the Bay of Bengal: Its fate and role in air-sea heat exchange. Oceanography 29(2):72–81, http://dx.doi.org/10.5670/oceanog.2016.40.

Marshall, J., A. Adcroft, C. Hill, L. Perelman, and C. Heisey. 1997. A finite-volume, incompress-ible Navier-Stokes model for studies of the ocean on parallel computers. Journal of Geophysical Research 102(C3):5,753–5,766.

McPhaden, M.J. 1993. TOGA-TAO and the 1991–93 El Niño-Southern Oscillation event. Oceanography 6(2):36–44, http://dx.doi.org/ 10.5670/oceanog.1993.12.

McPhaden, M.J., G. Meyers, K. Ando, Y. Masumoto, V.S.N. Murty, M. Ravichandran, F. Syamsudin, J. Vialard, L. Yu, and W. Yu. 2009. RAMA: The Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction. Bulletin of American Meteorological Society 90:459–480, http://dx.doi.org/10.1175/2008BAMS2608.1.

Narvekar, J., and S. Prasanna Kumar. 2006. Seasonal variability of the mixed layer in the cen-tral Bay of Bengal and associated changes in nutrients and chlorophyll. Deep Sea Research Part I 53:820–835, http://dx.doi.org/10.1016/ j.dsr.2006.01.012.

Menemenlis, D., J.M. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguyen, M. Schodlock, and H. Zhang. 2008. ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter #31:13–21, http://ecco2.org/manuscripts/reports/ECCO2_Mercator.pdf.

Packnowski, R.C., and S.G.H. Philander. 1981. Parameterization of vertical mixing in numer-ical models of the tropical ocean. Journal of Physical Oceanography 11:1,442–1,451, http://dx.doi.org/10.1175/1520-0485(1981)011 <1443:POVMIN>2.0.CO;2.

Oceanography | June 2016 49

Pan, H.L., and W.S. Wu. 1995. Implementing a Mass Flux Convective Parameterization Package for the NMC Medium-Range Forecast Model. NOAA National Meteorological Center Office Note 409, 40 pp.

Papa, F., C. Prigent, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. 2010. Interannual variabil-ity of surface water extent at global scale. Journal of Geophysical Research 115, D12111, http://dx.doi.org/10.1029/2009JD012674.

Parekh, A., J.S. Chowdary, O. Sayantani, T. Fousiya, and C. Gnanaseelan. 2016. Tropical Indian Ocean surface salinity bias in Climate Forecasting System coupled models and the role of upper ocean processes. Climate Dynamics 46:2,403–2,422, http://dx.doi.org/10.1007/s00382-015-2709-8.

Prasad, T.G. 1997. Annual and seasonal mean buoy-ancy fluxes for the tropical Indian Ocean. Current Science 73:667–674.

Praveen Kumar, B., J. Vialard, M. Lengaigne, V.S.N. Murty, and M.J. McPhaden. 2010. TropFlux: Air-sea fluxes for the global tropical oceans: Description and evaluation against obser-vations. Climate Dynamics 38:1,521–1,543, http://dx.doi.org/10.1007/s00382-011-1115-0.

Rahaman, H., M. Ravichandran, D. Sengupta, M.J. Harrison, and S.M. Griffies. 2014. Development of a regional model for the North Indian Ocean. Ocean Modelling 75:1–19, http://dx.doi.org/10.1016/ j.ocemod.2013.12.005.

Rao, R.R., and R. Sivakumar. 1999. On the pos-sible mechanisms of the evolution of a mini-warm pool during the pre-summer monsoon season and the genesis of onset vortex in the southeastern Arabian Sea. Quarterly Journal of Royal Meteorological Society 125:787–809, http://dx.doi.org/10.1002/qj.49712555503.

Rao, R.R., and R. Sivakumar. 2003. Seasonal vari-ability of sea surface salinity and salt budget of the mixed layer of the north Indian Ocean. Journal of Geophysical Research 108(C1), 3009, http://dx.doi.org/10.1029/2001JC000907.

Ravichandran, M., D. Behringer, S. Sivareddy, M.S. Girishkumar, N. Chacko, and R. Harikumar. 2013. Evaluation of the Global Ocean Data Assimilation System at INCOIS: The Tropical Indian Ocean. Ocean Modelling 69:123–135, http://dx.doi.org/10.1016/j.ocemod.2013.05.003.

Roxy, M. 2014. Sensitivity of precipitation to sea surface temperature over the tropical summer monsoon region and its quantification. Climate Dynamics 43:1,159–1,169, http://dx.doi.org/10.1007/s00382-013-1881-y.

Saha, S., S. Moorthi, X. Wu, J. Wang, S. Nadiga, P. Tripp, D. Behringer, Y.-T. Hou, H-y. Chuang, M. Iredell, and others. 2014. The NCEP Climate Forecast System Version 2. Journal of Climate 27:2,185–2,208, http://dx.doi.org/10.1175/JCLI-D-12-00823.1.

Saha, S., and S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H.M. Van den Dool, H.-L. Pan, S. Moorthi, D. Behringer, and others. 2006. The NCEP Climate Forecast System. Journal of Climate 19:3,483–3,517, http://dx.doi.org/10.1175/JCLI3812.1.

Sahai, A.K., S. Sharmila, S. Abhilash, R. Chattopadhyay, N. Borah, R.P.M. Krishna, J. Susmitha, M. Roxy, S. De, and S. Pattnaik. 2013. Simulation and extended range predic-tion of monsoon intraseasonal oscillations in NCEP CFS/GFS version 2 framework. Current Science 104:1,394–1,408.

Sanilkumar, K., N. Mohankumar, M. Joseph, and R. Rao. 1994. Genesis of meteorologi-cal disturbances and thermohaline variabil-ity of the upper layers in the head of the Bay of Bengal during Monsoon Trough Boundary

Layer Experiment (MONTBLEX-90). Deep Sea Research Part I 41:1,569–1,581, http://dx.doi.org/ 10.1016/0967-0637(94)90061-2.

Sarkar, S., H.T. Pham, S. Ramachandran, J.D. Nash, A. Tandon, J. Buckley, A.A. Lotliker, and M.M. Omand. 2016. The interplay between submesoscale instabili-ties and turbulence in the surface layer of the Bay of Bengal. Oceanography 29(2):146–157, http://dx.doi.org/10.5670/oceanog.2016.47.

Schott, F., and J.P. McCreary. 2001. The mon-soon circulation of the Indian Ocean. Progress in Oceanography 51:1–123, http://dx.doi.org/10.1016/S0079-6611(01)00083-0.

Sengupta, D., G.N. Bharath Raj, M. Ravichandran, J. Sree Lekha, and F. Papa. 2016. Near-surface salinity and stratification in the north Bay of Bengal from moored observations. Geophysical Research Letters 43:4,448–4,456, http://dx.doi.org/10.1002/2016GL068339.

Sengupta, D., G.N. Bharath Raj, and S.S.C. Shenoi. 2006. Surface freshwater from Bay of Bengal run-off and Indonesian throughflow in the tropical Indian Ocean. Geophysical Research Letters 33, L22609, http://dx.doi.org/10.1029/2006GL027573.

Servain, J., A. Busalacchi, M.J. McPhaden, A.D. Moura, G. Reverdin, M. Vianna, and S. Zebiak. 1998. A Pilot Research Moored Array in the Tropical Atlantic (PIRATA). Bulletin of the American Meteorology Society 79:2,019–2,031, http://dx.doi.org/10.1175/1520-0477(1998)079<2019:APRMAI>2.0.CO;2.

Seo, H., S.P. Xie, R. Murtugudde, M. Jochum, and A.J. Miller. 2009. Seasonal effects of Indian Ocean freshwater forcing in a regional cou-pled model. Journal of Climate 22:6,577–6,596, http://dx.doi.org/10.1175/2009JCLI2990.1.

Shankar, D., P.N. Vinayachandran, and A.S. Unnikrishnan. 2002. The monsoon cur-rents in the north Indian Ocean. Progress in Oceanography 52:63–120, http://dx.doi.org/10.1016/S0079-6611(02)00024-1.

Shenoi, S.S.C., D. Shankar, and S.R. Shetye. 2002. Differences in heat budgets of the near surface Arabian Sea and Bay of Bengal: Implications for the summer monsoon. Journal of Geophysical Research 107, 3052, http://dx.doi.org/10.1029/2000JC000679.

Subramanian, V. 1993. Sediment load of Indian rivers. Current Science 64:928–930.

Thompson, B., C. Gnanaseelan, and P.S. Salvekar. 2006. Variability in the Indian Ocean circula-tion and salinity and its impact on SST anom-alies during dipole events. Journal of Marine Research 64:853–880, http://dx.doi.org/ 10.1357/002224006779698350.

Venkatesan, R., V.R. Shamji, G. Latha, S. Mathew, R.R. Rao, A. Muthiah, and M.A. Atmanand. 2013. In situ ocean subsurface time-series measurements from OMNI buoy network in the Bay of Bengal. Current Science 104(9):1,166–1,177.

Vinayachandran, P.N., J.P. McCreary, R.R. Hood, and K.E. Kohler. 2005. A numerical inves-tigation of the phytoplankton bloom in the Bay of Bengal during northeast monsoon. Journal of Geophysical Research 110, C12001, http://dx.doi.org/10.1029/2005JC002966.

Vinayachandran, P.N., and R.S. Nanjundiah. 2009. Indian Ocean sea surface salinity variations in a coupled model. Climate Dynamics 33:245–263, http://dx.doi.org/10.1007/s00382-008-0511-6.

Vinayachandran, P.N., D. Shankar, S. Vernekar, K.K. Sandeep, P. Amol, C.P. Neema, and A. Chatterjee. 2013. A summer monsoon pump to keep the Bay of Bengal salty. Geophysical Research Letters 40:1,777–1,782, http://dx.doi.org/ 10.1002/grl.50274.

Wijesekera, H.W., E. Shroyer, A. Tandon, M. Ravichandran, D. Sengupta, S.U.P. Jinadasa, H.J.S. Fernando, N. Agrawal, K. Arulanathan, G.S. Bhat, and others. In press. ASIRI: An Ocean-Atmosphere Initiative for Bay of Bengal. Bulletin of the American Meteorological Society, http://dx.doi.org/10.1175/BAMS-D-14-00197.1.

Wilson, E.A., and S.C. Riser. 2016. An assess-ment of the seasonal salinity budget for the upper Bay of Bengal. Journal of Physical Oceanography 46(5):1,361–1,376, http://dx.doi.org/10.1175/JPO-D-15-0147.1.

Zweng, M.M, J.R. Reagan, J.I. Antonov, R.A. Locarnini, A.V. Mishonov, T.P. Boyer, H.E. Garcia, O.K. Baranova, D.R. Johnson, D. Seidov, and M.M. Biddle. 2013. World Ocean Atlas 2013, Volume 2: Salinity. S. Levitus, ed., A. Mishonov, technical ed., NOAA Atlas NESDIS 74, 39 pp.

ACKNOWLEDGMENTSWe thank the Director of the IITM for support. TSF acknowledges the support of CSIR, India, for the JRF/SRF Fellowship. We sincerely thank anonymous reviewers for valuable comments that helped us to improve the manuscript. We wish to thank M.K. Roxy for providing CFSv2 data. HS is grateful for support from the ONR Young Investigator Program (N00014-15-1-2588). Figures were prepared using Ferret. In this study, temperature and salinity data were obtained from World Ocean Atlas 2013 (https://www.nodc.noaa.gov/OC5/woa13/woa13data.html). Zonal and meridional velocity components were taken from Estimating the Circulation and Climate of the Ocean (http://ecco2.jpl.nasa.gov/opendap/data1/cube/cube92/lat_lon/quart_90S_90N/contents.html), sur-face latent fluxes from INCOIS-TropFlux (http://www.incois.gov.in/tropflux_datasets/data/monthly), and precipitation from Global Precipitation Climatology Project (GPCP; http://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html). Ocean analysis data from INCOIS-GODAS was obtained from the INCOIS ftp server (ftp://ftpser.incois.gov.in).

AUTHORSJasti S. Chowdary is Scientist, Indian Institute of Tropical Meteorology, Pune, India. G. Srinivas is Scientist, Indian Institute of Tropical Meteorology, Pune, India. T.S. Fousiya is a graduate student at the Indian Institute of Tropical Meteorology, Pune, India. Anant Parekh is Scientist, Indian Institute of Tropical Meteorology, Pune, India. C. Gnanaseelan (seelan@tropmet.res.in) is Scientist, Indian Institute of Tropical Meteorology, Pune, India. Hyodae Seo is Associate Scientist, Woods Hole Oceanographic Institution, Woods Hole, MA, USA. Jennifer A. MacKinnon is Professor, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA.

ARTICLE CITATIONChowdary, J.S., G. Srinivas, T.S. Fousiya, A. Parekh, C. Gnanaseelan, H. Seo, and J.A. MacKinnon. 2016. Representation of Bay of Bengal upper-ocean salinity in general cir-culation models. Oceanography 29(2):38–49, http://dx.doi.org/10.5670/oceanog.2016.37.